Noud de Kroon has joined the UvA in October 2018 as a PhD student of AMLab, under the joint supervision of dr. Joris Mooij and dr. Danielle Belgrave (Microsoft Research Cambridge). Previously, he obtained a bachelor’s degree in software science at Eindhoven University of Technology and a master’s degree in computer science at the University of Oxford. His research focus is on combining causality and reinforcement learning in order to make better

decisions and improve data efficiency, with applications for example in the medical domain.

# Author Archives: Joris Mooij

# Vacancy: PhD position in Reinforcement Learning

For more information on this vacancy, see Vacancies.

# Vacancy: Assistant Professor in Deep Reinforcement Learning

AMLab is looking for an assistant professor in deep reinforcement learning.

# Vacancy: Postdoctoral Researcher in Causal Inference

A vacancy for a postdoctoral researcher is available to work with dr. Joris Mooij on the development and validation of new methods for causal modelling, reasoning and discovery.

# Tineke Blom joins AMLab

Tineke Blom joined AMLab as a PhD student on September 1st. Tineke studied Mathematical Sciences at Utrecht University and her research interests include causality, applied mathematics, and learning algorithms. She will conduct research on causality as part of the ERC starting grant project CAFES led by Joris Mooij.

# First prize in CRM Causal Inference Challenge

An interdisciplinary team of AMLAB researchers, a biologist and a doctor won the first prize in the CRM Causal Inference Challenge (part of the Workshop Statistical Causal Inference and its Applications to Genetics, July 25 – August 19, Montreal, Canada). The team was led by Joris Mooij and consisted of AMLAB members Tom Claassen, Sara Magliacane, Philip Versteeg, Stephan Bongers, Thijs van Ommen, Patrick Forre, and external researchers Renée van Amerongen (Swammerdam Institute for Life Sciences) and Lucas van Eijk (Radboud University Medical Center). The task of the challenge was to predict values of certain phenotypic variables of knockout mice, given data from wildtype and other knockout mice.

# Talk by Joris Mooij

You are all cordially invited to the AMLab colloquium on **April 26 at 16:00 in C3.163**, where** Joris Mooij** will give a talk titled “**Automating Causal Discovery and Prediction**“. Afterwards there are drinks and snacks!

**Abstract**: The discovery of causal relationships from experimental data and the construction of causal theories to describe phenomena are fundamental pillars of the scientific method. How to reason effectively with causal models, how to learn these from data, and how to obtain causal predictions has been traditionally considered to be outside of the realm of statistics. Therefore, most empirical scientists still perform these tasks informally, without the help of mathematical tools and algorithms. This traditional informal way of causal inference does not scale, and this is becoming a serious bottleneck in the analysis of the outcomes of large-scale experiments nowadays. In this talk I will describe formal causal reasoning methods and algorithms that can help to automate the process of scientific discovery from data.

# Tom Claassen joins AMLab

Tom Claassen joined AMLab as a parttime postdoc (50%). Tom studied physics in Twente and worked for several years as a Systems Architect before doing his PhD on causal discovery and logic at the Radboud University Nijmegen. Tom will work on causality as a team member of the VIDI project of Joris Mooij.

# Talk by Martin Gullaksen

You are all cordially invited to a presentation on Friday, April 8th, from 16:00-17:00 in **C1.112** by **Martin Gullaksen** on his master’s thesis entitled “**Probabilistic Spatio-Temporal Inference in Early Embryonic Development. The case of Drosophila Melanogaster**“.** **

**Abstract**: Being able to infer gene regulatory networks from spatio-temporal expression

data is a major problem in biology. This thesis proposes a new dynamic Bayes

networks approach, which we benchmark by using the well researched gap gene

problem of the Drosophila melanogaster, with the capability of realistically

inferring gene regulatory networks and producing high quality simulations. The

thesis solves practical issues, currently associated with spatio-temporal gene

inference, such as computational time and parameter fragility, while obtaining

a similar gene regulatory network and matrix as our ground truth network. The

proposed modelling framework computes the gene regulatory network in 10-15

second on a modern laptop. Effectively removing the computational barrier of

the problem and allowing for future gene regulatory networks of greater gene

count to be processed. Besides producing a gene regulatory matrix our method

also produces high quality simulations of the gene activation levels of the gap

gene problem. In addition, unlike many competing problem formulations, the

proposed model is probabilistic in nature, hence allowing statistical inference

to be made. Finally, using Bayesian statistics, we perform robustness tests on

the topology of our proposed gene regulatory network and our regulatory

weights.

# Talk by Errol Zalmijn (ASML)

You are all cordially invited to a presentation on Wednesday, March 9, at 11:00 in **C3.163** by **Errol Zalmijn**, data analyst at ASML, on “**Transfer entropy: an information signature of causation in ASML lithographic time series analysis**“.** **

**Abstract**: Considering the ASML lithography system to be a complex, distributed computing system that can be modeled as a network of driving and responding or driven observables i.e. cause-and-effect relationships, transfer entropy (Schreiber, 2000), an information-theoretic measure of time-directed information transfer between jointly dependent processes, enables detection of causal interactions between simultaneously observed time series from lithographic system data. Being a non-parametric measure, capable of identifying arbitrary linear and non-linear causal effects, transfer entropy can effectively gain a better understanding of the underlying system dynamics, a prerequisite for accurate diagnosis and prognosis, as well as structural design improvements.